Mapping intertidal oyster farms using unmanned aerial vehicles (UAV) high-resolution multispectral data

In France, oyster aquaculture has been historically developed in intertidal zones, with shellfish farming areas covering much of the Atlantic coast. Monitoring these off-bottom cultures where oysters are grown in plastic mesh-bags set on trestle tables is mandatory for the maritime administration to check compliance with a Structural Plan Document (SPD), while also being important for stock assessment in relation to carrying capacity issues. However, traditional monitoring methods are time-consuming, labor-intensive, and inefficient in covering large intertidal areas. In this study, we used a new GIS-based analytical method to assess the potential of high-resolution Unmanned Aerial Vehicle (UAV) multispectral data to retrieve spatial information on oyster-farming structures using Bourgneuf Bay (France) as a case-study. A non-parametric machine learning algorithm was applied to four UAV flight orthomosaics collected at different altitudes (12, 30, 50, and 120 m) to identify oyster mesh-bags. These supervised classifications achieved overall accuracies above 95% for all tested altitudes. In addition, an accurate distinction of oyster-bag mesh sizes (4, 9 and 14 mm) was obtained for 12–50 m flights, but there was a lower accuracy at 120 m. Across all flights, the 4 mm mesh size was the least well detected (72.14% Producer Accuracy). This information can be used to identify bags with specific mesh-sizes used for spat or adult grow out. Finally, we accurately measured oyster table heights using a high-resolution Digital Surface Model (DSM) derived from Structure from Motion (SfM) photogrammetry. The 50 m flight was suggested as the best compromise to obtain precise measurements of the oyster table heights while covering larger areas than lower altitude flights. This demonstrates that UAV technology can provide a set of spatial variables relevant for shellfish farmers and coastal managers in an efficient, rapid, and non-destructive way to monitor the extent and characteristics of oyster-farming areas regularly.

This research was funded by grants PY20-00244 SAT4ALGAE by Junta de Andalucía. A.R. is supported by grant FPU19/04557 funded by Ministry of Universities of the Spanish Government. H.P. was supported by the Erasmus+: Key Action 1 – Erasmus Mundus Joint Master Degrees (EMJMD) (Grant No. 599111-EPP-1-2018-1-EL-EPPKA1-JMD-MOB) for the EMJMD in Aquaculture, Environment and Society PLUS (ACES+). This work represents a contribution to CSIC Thematic Interdisciplinary Platform PTI TELEDETECT.

Peer reviewed